Registering an object with respect to a robot is performed in many industrial assembly tasks, such as grasping and insertion. Registering refers to finding a relative pose between the object and the robot. Probing-based registration methods use a probe attached to the robot to measure locations of contact with the object and use the locations to register the robot with respect to the object.
Some conventional methods use probing and particle-based Monte-Carlo localization to solve the registration problem. For example, one method describes a localization using probing and particle filtering for a lock and key assembly. As a preprocessing step, that method densely probes a grid of (x, y) locations on the object with the probe to obtain a dense set of contact locations (x, y, z), generating a contact configuration-space map, which describes possible poses wherein the probe has contact with the object. Then, the method performs particle filtering by probing the object sequentially, using the sequence of contact locations obtained from the probing. Likelihoods are evaluated using the contact configuration-space map.
Another method that uses a preprocessing step of densely probing the object uses a force and torque sensor connected to the probe to generate a force-torque map. The force-torque map includes the contact force and torque at every possible pose at which there is contact between the probe and the object. That method also describes estimating the force-torque map directly from a computer-aided design (CAD) model. Using the force-torque map, which was acquired in advance by dense probing, particle filtering is used with sequential probing to match the force-torque measurements to the map. That method also optionally incorporates observations from a camera.
To reduce the number of particles required for 6-degrees-of-freedom (6-DOF) registration, another method uses a coarse-to-fine approach by assigning a region, instead of a single point, to each particle. That method first determines regions of high likelihood at a coarse resolution by increasing the measurement uncertainty, and then iteratively resamples particles inside the regions while reducing the uncertainty.
Given a set of point measurements and a 3D model of the object as a set of planes, some methods use three correspondences between 3D planes and 3D points with normals for 6-DOF registration. One method uses six 3D point to 3D plane correspondences. Each of these methods uses the minimum number of correspondences and is suitable as a hypothesis generator in hypothesis-and-test frameworks such as RANdom SAmple Consensus (RANSAC). Another method that uses point-to-plane correspondences utilizes branch-and-bound optimization to achieve a globally optimal registration. Iterative closest point (ICP) methods often do not reach a global optimum unless they have a good initial estimate of the registration. Although those offline methods provide solutions to the point-plane registration problem, they assume that a complete set of contact measurements has already been collected. They provide no method for selecting a good next motion based on an already-collected subset of contact measurements, nor do they provide a termination condition for determining when enough measurements have been collected.
To our knowledge, the general problem of motion selection, i.e., planning where to probe next, has not previously been addressed in probing-based registration. However, motion selection is known in mobile robotics, such as in active simultaneous localization and mapping (active SLAM) applications, in which a method plans the next motion of a mobile robot.
In mobile robotics, selecting the next motion is often addressed using entropy-based measures, such as expected information gain, or reduction in entropy, which measure a reduction in uncertainty. However, entropy estimation methods that are typically used in conventional mobile robotics, such as determining the entropy of a particle distribution using a Gaussian approximation, are not well suited to probing-based registration.